Expertise and Research Interests:

Feng Yang, Ph.D., joined the Lister Hill National Center for Biomedical Communications (LHNCBC), National Library of Medicine (NLM) in October 2017, and is currently a Research Fellow in NLM. She is also a visiting Professor at Guizhou University. Dr. Yang had been working as a Principal Investigator, Associate Professor at Beijing Jiaotong University in China from 2012 to 2019. Dr. Yang received her Ph.D. degree from the National Institute of Applied Science (INSA Lyon) in France in 2011, and her B.S. and M.S. degrees from Northwestern Polytechnical University in China in 2005 and 2007, respectively. Her current research interests include machine learning and artificial intelligence-based biomedical data processing and analysis. She has so far published more than 90 research papers, including 40 journal articles, 1 book chapter, and 50 conference proceedings. She has been the organizing committee member of the IEEE ICSP special session on “Medical Image Processing and Understanding” and served as the special session chairman from 2012 to 2022. She has been an organizing committee member of the MICCAI workshop on Medical Image Learning with Limited and Noisy Data (MILLanD).

Honors and Awards:

Dr. Feng Yang received the NLM Special Act/Service Award on Image-based Machine Learning and Artificial Intelligence, National Library of Medicine in 2022.

Dr. Feng Yang received the NLM Special Acts/Services Group Award in 2018.

Publications:

Rajaraman S, Zamzmi G, Yang F, Liang Z, Xue Z, Antani SK. Semantically redundant training data removal and deep model classification performance: A study with chest X-rays. Computerized Medical Imaging and Graphics. Volume 115, 2024, 102379, ISSN 0895-6111, https://doi.org/10.1016/j.compmedimag.2024.102379.

Rajaraman S, Zamzmi G, Yang F, Liang Z, Xue Z, Antani SK. Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images. PLOS Digital Health 3(1): e0000286. https://doi.org/10.1371/journal.pdig.0000286.

Rajaraman S, Yang F, Zamzmi G, Xue Z, Antani S. Can deep adult lung segmentation models generalize to the pediatric population? Expert Systems with Applications, Volume 229, Part A, 2023, 120531, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120531.

Bui VCB, Yaniv Z, Harris M, Yang F, Kantipudi K, Hurt D, Rosenthal A, Jaeger S. Combining Radiological and Genomic TB Portals Data for Drug Resistance Analysis. IEEE Access. 2023;11:84228-84240. doi: 10.1109/access.2023.3298750. Epub 2023 Jul 25. PMID: 37663145; PMCID: PMC10473876.

Oguguo T, Zamzmi G, Rajaraman S, Yang F, Xue Z, Antani S. A Comparative Study of Fairness in Medical Machine Learning. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 2023, pp. 1-5, doi: 10.1109/ISBI53787.2023.10230368.

Xue Z, Yang F, Rajaraman S, Zamzmi G, Antani S. Cross Dataset Analysis of Domain Shift in CXR Lung Region Detection. Diagnostics. 2023; 13(6):1068. https://doi.org/10.3390/diagnostics13061068.

Karki M, Kantipudi K, Haghighi B, Bui V, Yang F,Yu H, Harris M, Kassim YM, Hurt DE, Rosenthal A, Yaniv Z, Jaeger S. Training Data for Machine Learning to Enhance Patient-Centered Outcomes Research (PCOR) Data Infrastructure — A Case Study in Tuberculosis Drug Resistance.

Yang F, Zamzmi G, Angara S, Rajaraman S, Aquilina A, Xue Z, Jaeger S, Papagiannakis E, Antani SK. Assessing Inter-Annotator Agreement for Medical Image Segmentation. IEEE Access, doi: 10.1109/ACCESS.2023.3249759.

Rajaraman S, Yang F, Zamzmi G, Xue Z, Antani SK. Assessing the Impact of Image Resolution on Deep Learning for TB Lesion Segmentation on Frontal Chest X-rays. Diagnostics. 2023; 13(4):747. https://doi.org/10.3390/diagnostics13040747.

Yu H, Mohammed FO, Hamid MA, Yang F, Kassim YM, Mohamed AO, Maude RJ, Ding XC, Owusu ED, Yerlikaya S, Dittrich S, Jaeger S . Patient-level performance evaluation of a smartphone-based malaria diagnostic application. Malar J 22, 33 (2023). https://doi.org/10.1186/s12936-023-04446-0.

Rajaraman S, Zamzmi G, Yang F, Xue Z, Antani SK. Data Characterization for Reliable AI in Medicine. Recent Trends Image Process Pattern Recogn (2022). 2023;1704:3-11. doi: 10.1007/978-3-031-23599-3_1. Epub 2023 Jan 11. PMID: 36780238; PMCID: PMC9912175.

Chen Q, Wang L, Guo S, Xia H, Yang F, Zhu Y. Glioma grade prediction using a cross-fusion network based on unsegmented multi-sequence magnetic resonance images. 2022 16th IEEE International Conference on Signal Processing (ICSP), 2022, pp. 447-451. doi: 10.1109/ICSP56322.2022.9965327. (Best paper award)

He Y, Wang L, Yang F, Clarysse P, Robini M, Zhu Y. Effect of different configurations of diffusion gradient direction on accuracy of diffusion tensor estimation in cardiac DTI. 2022 16th IEEE International Conference on Signal Processing (ICSP), 2022, pp. 437-441. (Best paper award)

Rajaraman S, Yang F, Zamzmi G, Xue Z, Antani SK. A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs. Bioengineering. 2022; 9(9):413. https://doi.org/10.3390/bioengineering9090413.

Yang F, Lu PX, Deng M, Wáng YXJ, Rajaraman S, Xue Z, Folio LR, Antani SK, Jaeger S. Annotations of Lung Abnormalities in the Shenzhen Chest X-ray Dataset for Computer-Aided Screening of Pulmonary Diseases. Data 2022, 7, 95. https://doi.org/10.3390/data7070095.

Rajaraman S, Zamzmi G, Yang F, Xue Z, Jaeger S, Antani SK. Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays. Biomedicines 2022, 10, 1323. https://doi.org/10.3390/biomedicines10061323.

Wang L, Hong Y, Qin YB, Cheng XY, Yang F, Yang J, Zhu YM. Connecting macroscopic diffusion metrics of cardiac diffusion tensor imaging and microscopic myocardial structures based on simulation. Med Image Anal. 2022 Apr;77:102325. doi: 10.1016/j.media.2021.102325. Epub 2022 Feb 5.

Karki M, Kantipudi K, Yang F, Yu H, Wang xY, Yaniv Z, Jaeger S. Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays. Diagnostics (Basel). 2022 Jan 13;12(1):188. doi: 10.3390/diagnostics12010188. PMID: 35054355; PMCID: PMC8775073.

Xie B, Zhu Y, Niu P, Su T, Yang F, Wang L, Rodesch PA, Boussel L, Douek P, Duvauchelle P. Super-Energy-Resolution Material Decomposition for Spectral Photon-Counting CT Using Pixel-Wise Learning. IEEE Access, vol. 9, pp. 168485-168495, 2021, doi: 10.1109/ACCESS.2021.3134636.

Karki M, Kantipudi K, Yu H, Yang F, Kassim Y, Yaniv Z,Jaeger S. Identifying Drug-Resistant Tuberculosis in Chest Radiographs: Evaluation of CNN Architectures and Training Strategies. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, accepted on July 15th, 2021, will be held virtually October 31 – November 4, 2021.

Kassim YM, Yang F, Yu H, Maude RJ, Jaeger S. Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images. Diagnostics (Basel). 2021 Oct 27;11(11):1994. doi: 10.3390/diagnostics11111994. PMID: 34829341; PMCID: PMC8621537.

Ufuktepe DK, Yang F, Kassim YM, Yu H, Maude RJ, Palaniappan K, Jaeger S. Deep Learning-Based Cell Detection and Extraction in Thin Blood Smears for Malaria Diagnosis. 50th Annual IEEE AIPR 2021, held virtually October 12-14, 2021.

Yang F, Yu H, Kantipudi K, Rosenthal A, Hurt DE, Yaniv Z, Jaeger S. Automated Drug-Resistant TB Screening: Importance of Demographic Features and Radiological Findings in Chest X-Ray. 50th Annual IEEE AIPR 2021, held virtually October 12-14, 2021.

Yang F, Yu H, Kantipudi K, Rosenthal A, Hurt D, Antani S, Yaniv ZR, Jaeger S. Differentiating between Drug-Sensitive and Drug-Resistant Tuberculosis with Machine Learning for Clinical and Radiological Features. Quantitative Imaging in Medicine and Surgery, 0(0): 1–16, 2021.

He Y, Wang L, Yang F, Xia Y, Clarysse P, Zhu Y. Systematic Study of Joint Influence of Angular Resolution and Noise in Cardiac Diffusion Tensor Imaging. Ennis D.B., Perotti L.E., Wang V.Y. (eds) Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science, vol 12738. Springer, Cham. https://doi.org/10.1007/978-3-030-78710-3_20.

Niu P, Wang L, Xie B, Robini M, Boussel, L Douek P, Zhu Y, Yang F. Improved Image Reconstruction Using Multi-Energy Information in Spectral Photon-Counting CT. IEEE Access, vol. 9, pp. 97981-97989, 2021, doi: 10.1109/ACCESS.2021.3083505.

Kassim YM, Palaniappan K, Yang F, Poostchi M, Palaniappan N, Maude RJ, Antani S, Jaeger S. Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears. IEEE J Biomed Health Inform. 2021 May;25(5):1735-1746. doi: 10.1109/JBHI.2020.3034863. Epub 2021 May 11.

Li Z, Wang X, Wang L, Ji W, Zhang M, Zhu Y, Yang F. UNet-ESPC-Cascaded Super-Resolution Reconstruction in Spectral CT. 2020 15th IEEE International Conference on Signal Processing (ICSP).

Yu H, Yang F, Rajaraman S, Ersoy I, Moallem G, Poostchi M, Palaniappan K, Antani S, Maude RJ, Jaeger S. Malaria Screener: a smartphone application for automated malaria screening. BMC Infect Dis. 2020 Nov 11;20(1):825. doi: 10.1186/s12879-020-05453-1.

Robini MC, Yang F, Zhu Y. A stochastic approach to full inverse treatment planning for charged-particle therapy. J Glob Optim 77, 853–893 (2020). https://doi.org/10.1007/s10898-020-00902-2.

Yang F, Quizon N, Silamut K, Maude RJ, Jaeger S, Antani SK. Cascading YOLO: Automated Malaria Parasite Detection for Plasmodium Vivax in Thin Blood Smears. Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141Q (16 March 2020); https://doi.org/10.1117/12.2549701.

Yu H, Yang F, Silamut R, Maude S, Jaeger S, Antani SK. Automatic Blood Smear Analysis with Artificial Intelligence and Smartphones [Poster]. ASTMH 68th Annual Meeting, Washington DC, Nov. 20-24, 2019.

Yang F, Poostchi M, Silamut K, Maude RJ, Jaeger S, Thoma G. Automated Parasite Classification of Malaria on Thick Blood Smears [Poster]. ASTMH 67th Annual Meeting, New Orleans, LA, Oct. 28 – Nov. 1, 2018.

Yang F, Yu H, Silamut K, Maude RJ, Jaeger S, Antani SK. Parasite Detection in Thick Blood Smears Based on Customized Faster-RCNN. Proceedings of AIPR2019, Washington DC, USA, Oct 15-17, 2019.

Yang F, Yu H, Silamut K, Maude R, Jaeger S, Antani SK. Smartphone-Supported Malaria Diagnosis Based on Deep Learning. Proceedings of 10th Workshop on Machine Learning in Medical Imaging (MLMI 2019) in conjunction with MICCAI, Shenzhen, China, Oct 13-17, 2019.

Yang F, Poostchi M, Yu H, Zhou Z, Silamut K, Yu J, Maude RJ, Jaeger S, Antani S. . Deep learning for smartphone-based malaria parasite detection in thick blood smears. IEEE J Biomed Health Inform. 2020 May;24(5):1427-1438. doi: 10.1109/JBHI.2019.2939121. Epub 2019 Sep 23.

Jaeger S, Juarez-Espinosa OH, Candemir S, Poostchi M, Yang F, Kim L, Ding M, Folio LR, Antani SK, Gabrielian A, Hurt D, Rosenthal A, Thoma GR. Detecting drug-resistant tuberculosis in chest radiographs. Int J Comput Assist Radiol Surg. 2018 Dec;13(12):1915-1925. doi: 10.1007/s11548-018-1857-9. Epub 2018 Oct 3.

Yang F, Yu H, Poostchi M, Silamut K, Maude RJ, Jaeger S. Smartphone-Supported Automated Malaria Parasite Detection. SIIM conference on Machine Intelligence in Medical Imaging, 2018.

Jaeger S, Antani SK, Rajaraman S, Yang F, Yu H. Malaria Screening: Research into Image Analysis and Deep Learning. Report to the Board of Scientific Counselors September 2018.